Configuration of application software on multi-core image processor

ABSTRACT

A method is described. The method includes calculating data transfer metrics for kernel-to-kernel connections of a program having a plurality of kernels that is to execute on an image processor. The image processor includes a plurality of processing cores and a network connecting the plurality of processing cores. Each of the kernel-to-kernel connections include a producing kernel that is to execute on one of the processing cores and a consuming kernel that is to execute on another one of the processing cores. The consuming kernel is to operate on data generated by the producing kernel. The method also includes assigning kernels of the plurality of kernels to respective ones of the processing cores based on the calculated data transfer metrics.

FIELD OF INVENTION

The field of invention pertains generally to the computing sciences and,more specifically, to the configuration of application software on amulti-core image processor.

BACKGROUND

Image processing typically involves the processing of pixel values thatare organized into an array. Here, a spatially organized two dimensionalarray captures the two dimensional nature of images (additionaldimensions may include time (e.g., a sequence of two dimensional images)and data type (e.g., colors). In a typical scenario, the arrayed pixelvalues are provided by a camera that has generated a still image or asequence of frames to capture images of motion. Traditional imageprocessors typically fall on either side of two extremes.

A first extreme performs image processing tasks as software programsexecuting on a general purpose processor or general purpose-likeprocessor (e.g., a general purpose processor with vector instructionenhancements). Although the first extreme typically provides a highlyversatile application software development platform, its use of finergrained data structures combined with the associated overhead (e.g.,instruction fetch and decode, handling of on-chip and off-chip data,speculative execution) ultimately results in larger amounts of energybeing consumed per unit of data during execution of the program code.

A second, opposite extreme applies fixed function hardwired circuitry tomuch larger units of data. The use of larger (as opposed to finergrained) units of data applied directly to custom designed circuitsgreatly reduces power consumption per unit of data. However, the use ofcustom designed fixed function circuitry generally results in a limitedset of tasks that the processor is able to perform. As such, the widelyversatile programming environment (that is associated with the firstextreme) is lacking in the second extreme.

A technology platform that provides for both highly versatileapplication software development opportunities combined with improvedpower efficiency per unit of data remains a desirable yet missingsolution.

SUMMARY

A method is described. The method includes calculating data transfermetrics for kernel-to-kernel connections of a program having a pluralityof kernels that is to execute on an image processor. The image processorincludes a plurality of processing cores and a network connecting theplurality of processing cores. Each of the kernel-to-kernel connectionsinclude a producing kernel that is to execute on one of the processingcores and a consuming kernel that is to execute on another one of theprocessing cores. The consuming kernel is to operate on data generatedby the producing kernel. The method also includes assigning kernels ofthe plurality of kernels to respective ones of the processing coresbased on the calculated data transfer metrics.

FIGURES

The following description and accompanying drawings are used toillustrate embodiments of the invention. In the drawings:

FIG. 1 shows a high level view of a stencil processor architecture;

FIG. 2 shows a more detailed view of an image processor architecture;

FIG. 3 shows an even more detailed view of an image processorarchitecture;

FIG. 4 shows an application software program that can be executed by animage processor;

FIGS. 5 and 6 show an embodiment for determining a configuration for theapplication software program of FIG. 4 to execute an image processor;

FIG. 7 shows a method for determining a configuration for an applicationsoftware program to execute on an image processor;

FIGS. 8a, 8b, 8c, 8d and 8e depict the parsing of image data into a linegroup, the parsing of a line group into a sheet and the operationperformed on a sheet with overlapping stencils;

FIG. 9a shows an embodiment of a stencil processor;

FIG. 9b shows an embodiment of an instruction word of the stencilprocessor;

FIG. 10 shows an embodiment of a data computation unit within a stencilprocessor;

FIGS. 11a, 11b, 11c, 11d, 11e,11f, 11g, 11h, 11i, 11j and 11k depict anexample of the use of a two-dimensional shift array and an executionlane array to determine a pair of neighboring output pixel values withoverlapping stencils;

FIG. 12 shows an embodiment of a unit cell for an integrated executionlane array and two-dimensional shift array;

FIG. 13 shows an exemplary computing system.

DETAILED DESCRIPTION 1.0 Unique Image Processor Architecture

As is known in the art, the fundamental circuit structure for executingprogram code includes an execution stage and register space. Theexecution stage contains the execution units for executing instructions.Input operands for an instruction to be executed are provided to theexecution stage from the register space. The resultant that is generatedfrom the execution stage's execution of an instruction is written backto the register space.

Execution of a software thread on a traditional processor entailssequential execution of a series of instructions through the executionstage. Most commonly, the operations are “scalar” in the sense that asingle resultant is generated from a single input operand set. Howeverin the case of “vector” processors, the execution of an instruction bythe execution stage will generate a vector of resultants from a vectorof input operands.

FIG. 1 shows a high level view of a unique image processor architecture100 that includes an array of execution lanes 101 coupled to atwo-dimensional shift register array 102. Here, each execution lane inthe execution lane array can be viewed as a discrete execution stagethat contains the execution units needed to execute the instruction setsupported by the processor 100. In various embodiments each executionlane receives a same instruction to execute in a same machine cycle sothat the processor operates as a two dimensional single instructionmultiple data (SIMD) processor.

Each execution lane has its own dedicated register space in acorresponding location within the two dimensional shift register array102. For example, corner execution lane 103 has its own dedicatedregister space in corner shift register location 104, corner executionlane 105 has its own dedicated register space in corner shift registerlocation 106, etc.

Additionally, the shift register array 102 is able to shift its contentsso that each execution lane is able to directly operate, from its ownregister space, upon a value that was resident in another executionlane's register space during a prior machine cycle. For example, a +1horizontal shift causes each execution lane's register space to receivea value from its leftmost neighbor's register space. On account of anability to shift values in both left and right directions along ahorizontal axis, and shift values in both up and down directions along avertical axis, the processor is able to efficiently process stencils ofimage data.

Here, as is known the art, a stencil is a slice of image surface areathat is used as a fundamental data unit. For example, a new value for aparticular pixel location in an output image may be calculated as anaverage of the pixel values in an area of an input image that theparticular pixel location is centered within. For example, if thestencil has a dimension of 3 pixels by 3 pixels, the particular pixellocation may correspond to the middle pixel of the 3×3 pixel array andthe average may be calculated over all nine pixels within the 3×3 pixelarray.

According to various operational embodiments of the processor 100 ofFIG. 1, each execution lane of the execution lane array 101 isresponsible for calculating a pixel value for a particular location inan output image. Thus, continuing with the 3×3 stencil averaging examplementioned just above, after an initial loading of input pixel data and acoordinated shift sequence of eight shift operations within the shiftregister, each execution lane in the execution lane array will havereceived into its local register space all nine pixel values needed tocalculate the average for its corresponding pixel location. That is, theprocessor is able to simultaneously process multiple overlappingstencils centered at, e.g., neighboring output image pixel locations.Because the processor architecture of FIG. 1 is particularly adept atprocessing over image stencils it may also be referred to as a stencilprocessor.

FIG. 2 shows an embodiment of an architecture 200 for an image processorhaving multiple stencil processors 202_1 through 202_N. As observed inFIG. 2, the architecture 200 includes a plurality of line buffer units201_1 through 201_M interconnected to a plurality of stencil processorunits 202_1 through 202_N and corresponding sheet generator units 203_1through 203_N through a network 204 (e.g., a network on chip (NOC)including an on chip switch network, an on chip ring network or otherkind of network). In an embodiment, any line buffer unit 201_1 through201_M may connect to any sheet generator 203_1 through 203_N andcorresponding stencil processor 202_1 through 201_N through the network204.

Program code is compiled and loaded onto a corresponding stencilprocessor 202 to perform the image processing operations earlier definedby a software developer (program code may also be loaded onto thestencil processor's associated sheet generator 203, e.g., depending ondesign and implementation). As such, each stencil processor 202_1through 202_N may be more generally characterized as a processing core,processor core, core and the like and the overall image processor may becharacterized as a multi-core image processor. In at least someinstances an image processing pipeline may be realized by loading afirst kernel program for a first pipeline stage into a first stencilprocessor 202_1, loading a second kernel program for a second pipelinestage into a second stencil processor 202_2, etc. where the first kernelperforms the functions of the first stage of the pipeline, the secondkernel performs the functions of the second stage of the pipeline, etc.and additional control flow methods are installed to pass output imagedata from one stage of the pipeline to the next stage of the pipeline.

In other configurations, the image processor may be realized as aparallel machine having two or more stencil processors 202_1, 202_2operating the same kernel program code. For example, a highly dense andhigh data rate stream of image data may be processed by spreading framesacross multiple stencil processors each of which perform the samefunction.

In yet other configurations, essentially any directed acyclic graph(DAG) of kernels may be loaded onto the image processor by configuringrespective stencil processors with their own respective kernel ofprogram code and configuring appropriate control flow hooks into thehardware to direct output images from one kernel to the input of a nextkernel in the DAG design.

As a general flow, frames of image data are received by a macro I/O unit205 and passed to one or more of the line buffer units 201 on a frame byframe basis. A particular line buffer unit parses its frame of imagedata into a smaller region of image data, referred to as a “line group”,and then passes the line group through the network 204 to a particularsheet generator. A complete or “full” singular line group may becomposed, for example, with the data of multiple contiguous completerows or columns of a frame (for simplicity the present specificationwill mainly refer to contiguous rows). The sheet generator furtherparses the line group of image data into a smaller region of image data,referred to as a “sheet”, and presents the sheet to its correspondingstencil processor.

In the case of an image processing pipeline or a DAG flow having asingle input, generally, input frames are directed to the same linebuffer unit 201_1 which parses the image data into line groups anddirects the line groups to the sheet generator 203_1 whose correspondingstencil processor 202_1 is executing the code of the first kernel in thepipeline/DAG. Upon completion of operations by the stencil processor202_1 on the line groups it processes, the sheet generator 203_1 sendsoutput line groups to a “downstream” line buffer unit 201_2 (in some usecases the output line group may be sent_back to the same line bufferunit 201_1 that earlier had sent the input line groups).

One or more “consumer” kernels that represent the next stage/operationin the pipeline/DAG executing on their own respective other sheetgenerator and stencil processor (e.g., sheet generator 203_2 and stencilprocessor 202_2) then receive from the downstream line buffer unit 201_2the image data generated by the first stencil processor 202_1. In thismanner, a “producer” kernel operating on a first stencil processor hasits output data forwarded to a “consumer” kernel operating on a secondstencil processor where the consumer kernel performs the next set oftasks after the producer kernel consistent with the design of theoverall pipeline or DAG.

As alluded to above with respect to FIG. 1, each stencil processor 202_1through 202_N is designed to simultaneously operate on multipleoverlapping stencils of image data. The multiple overlapping stencilsand internal hardware processing capacity of the stencil processoreffectively determines the size of a sheet. Again, as discussed above,within any of stencil processors 202_1 through 202_N, arrays ofexecution lanes operate in unison to simultaneously process the imagedata surface area covered by the multiple overlapping stencils.

Additionally, in various embodiments, sheets of image data are loadedinto the two-dimensional shift register array of a stencil processor 202by that stencil processor's corresponding (e.g., local) sheet generator203. The use of sheets and the two-dimensional shift register arraystructure is believed to effectively provide for power consumptionimprovements by moving a large amount of data into a large amount ofregister space as, e.g., a single load operation with processing tasksperformed directly on the data immediately thereafter by an executionlane array. Additionally, the use of an execution lane array andcorresponding register array provide for different stencil sizes thatare easily programmable/configurable. More details concerning theoperation of the line buffer units, sheet generators and stencilprocessors are provided further below in Section 3.0.

FIG. 3 shows a more detailed embodiment of a specific hardwareimplementation of the image processor of FIG. 2. As observed in FIG. 3,the network 204 of FIG. 2 is implemented in a ring topology 304 with a4×4 network node 314 at each intersection between a line buffer unit 301and sheet generator/stencil processor core 302. For simplicity, FIG. 3only labels the network node 314 that resides between line buffer unit301_4 and sheet generator/stencil processor core 302_4.

Here, each of sheet generator/stencil processor cores 302_1 through302_8 are understood to include both a stencil processor and itscorresponding sheet generator. For simplicity, each of the sheetgenerator/stencil processor cores 302_1 through 302_8 will hereinafterbe referred to simply as a stencil processor core or core. Althougheight line buffer units 301_1 through 301_8 and eight cores 302_1through 402_8 are depicted in the particular embodiment of FIG. 3 itshould be understood that different architectures are possible havingdifferent numbers of line buffer units and/or cores. Network topologiesother than a ring topology are also possible.

With respect to the image processor of FIG. 3, the ring network 304permits: 1) the I/O unit 305 to pass input data to any line buffer unit301_1 through 301_8 (or any core 302_1 through 302_8); 2) any linebuffer unit 301_1 to 301_8 to forward a line group to any core 302_1through 302_8; 3) any core 302_1 through 302_8 to pass its output datato any line buffer unit 301_1 through 301_8; and, 4) any line bufferunit 301_1 through 301_8 to pass image processor output data to I/O unit305. As such, a wealth of different software kernel loading options andinternal network configurations are possible. That is, theoretically,for any software application composed of multiple kernels to be executedon the various cores 302 of the processor, any kernel can be loaded ontoany core and any line buffer unit can be configured to source/sinkinput/output data to/from any core.

2.0 Configuration of Application Software on Image Processor

FIG. 4 shows an exemplary application software program or portionthereof that may be loaded onto the image processor of FIG. 3. Asobserved in FIG. 4, the program code may be expected to process one ormore frames of input image data 401 to effect some overalltransformation on the input image data 401. The transformation isrealized with the operation of one or more kernels of program code 402that operate on the input image data in an orchestrated sequencearticulated by the application software developer.

In the example of FIG. 4, the overall transformation is effected byfirst processing each input image with a first kernel K1. The outputimages produced by kernel K1 are then operated on by kernel K2. Each ofthe output images produced by kernel K2 are then operated on by kernelK3_1 or K3_2, The output images produced by kernel(s) K3_1/K3_2 are thenoperated on by kernel K4. In the particular example of FIG. 3, KernelsK3_1 and K3_2 may be, e.g., different kernels that perform differentimage processing operations (e.g., kernel K3_1 operates on input imagesof a first specific type and kernel K3_2 operates on input images of asecond, different type).

For simplicity only four kernels K1 through K4 are shown. In referenceto the image processor hardware architecture embodiment of FIG. 3, notethat, in a basic configuration where each kernel operates on a differentstencil processor, conceivably, four more kernels may flow from kernelK4 before all the cores 402 of the processor are executing a kernel (thefour kernel flow of FIG. 4 only utilizes half the cores of the processorof FIG. 3).

FIG. 4 also shows that different image sizes may be associated with thevarious kernel inputs/outputs. Here, as mentioned above, the imageprocessor receives a series of input frames 401. The size of each of theinput frames (e.g., the total number of pixels in any one frame) isdepicted as having a normalized size of unity (1.0). Kernel K1 operateson the input frames 401 to generate output frames 411 each having a sizethat is four times that of the input frames (the output frame size ofkernel K1 is shown having a size of 4.0). The increase in image size maybe effected, e.g., by kernel K1 performing up-sampling on the inputimage frames 401.

Kernel K2 processes the (larger) output image frames 411 generated bykernel K1 and generates smaller output image frames 412_1, 412_2 eachhaving a unity size (1.0). The decrease in size may be effected, e.g.,by kernel K2 performing down-sampling on the output images 411 of kernelK1. Kernel K3 operates on frames 412_1 to generate larger output frameshaving a normalized size of 4.0 while kernel K3_2 operates on frames412_2 to generate even larger output frames having a normalized size of5.0. Kernel K4 operates on the output images 413_1, 413_2 of kernelsK3_1 and K3_2 to generate output frames of unity size 1.0.

As can be seen from the example of FIG. 4, different amounts of data canbe passed between kernels depending on, e.g., the size of framesgenerated by a producing kernel that are processed by a consumingkernel. Here, referring back to the exemplary hardware implementation ofFIG. 3, it improves overall processor efficiency to place producing andconsuming kernels that pass large amounts of data between one other onstencil processors that are next to one another or at least close to oneanother in order to avoid passing large amounts of data for longdistances along the ring 304.

As such, recalling from the discussion of FIG. 3 that the processorimplementation 300 is extremely versatile in terms of the differentkernel-to-core placements and kernel-to-kernel interconnections it cansupport, it is pertinent to analyze the data flow of an applicationsoftware program to be configured to run on the processor 300 so thatits kernels can be placed on specific cores and its line buffers can beconfigured to source/sink specific kernels/cores such that larger sizeddata flows experience fewer hops along the network 304 and, e.g.,smaller sized data flows are permitted to experience more hops along thenetwork 304.

FIGS. 5 and 6 graphically depict some of the calculations of an affinitymapper software program that analyzes an application software programand maps its specific kernels to specific cores so that efficient dataflows are achieved within the processor. For ease of illustration, FIGS.5 and 6 demonstrate exemplary calculations for the application softwareflow of FIG. 4. As will be more apparent in the immediately followingdiscussion, the affinity mapper threads through different possiblecombinations of kernel-to-core placements in order to identify a moreoptimal overall configuration that maps each kernel of an applicationsoftware program to a particular processing core.

As part of these calculations, the mapper determines metrics for thevarious connections that indicate how inefficient or burdensome aparticular connection will be if implemented. In various embodiments,the mapper assigns weights to the various connections that are beinganalyzed, where, larger data transfers along more nodal hops and/orlonger distances along the network ring are assigned higher weights andsmaller data transfers along fewer nodal hops and/or shorter distancesalong the network ring are assigned smaller weights. Other possiblefactors may include, e.g., larger or smaller propagation delay along aconnection from, e.g., slower vs. faster transfer speeds.

Thus, more generically, higher weight corresponds to less internalprocessor data transfer efficiency and/or greater data transport burden.In various embodiments, the configuration that yields the lowest overallweight is ultimately chosen as the correct configuration for theprocessor. Alternate embodiments may choose to assign higher weights toless burdensome data transfers and attempt to find a configuration thatyields the highest total weight. For ease of discussion the remainder ofthe document will mainly describe an approach in which higher weightsare assigned to less efficient or more burdensome connections.Regardless, in various embodiments, a complete configuration correspondsto identifying: 1) which kernels operate on which cores; 2) which linebuffers source (feed) which kernels; and, 3) which line buffers sink(receive output data from) which kernels.

For simplicity, the exemplary affinity mapping process of FIGS. 5 and 6do not address the connection between the I/O unit 305 and the linebuffer unit that feeds input frames to the first kernel K1. FIG. 5outlines an exemplary set of calculations for the K1 to K2 kernelconnection of FIG. 4. Here, as discussed above, a first “producing”kernel (K1) operates on a first core and forwards its output data to aline buffer unit. The line buffer unit then forwards the data receivedfrom the first kernel (K1) to the second, “consuming” kernel (K2).

In an embodiment, in order to keep the size of the search spacecontained, and to simplify allocation of line buffer unit memoryresources, kernel-to-kernel connections are modeled by the mappingalgorithm without consideration of any intervening line buffer unit.That is, the presence of the line buffer units are initially ignored bythe mapping algorithm even though in actuality data transfers from aproducing kernel to a consuming kernel are essentially queued by anintervening line buffer unit.

FIG. 5 shows weight assignments for various configurations of kernel K1sending its output data to it consuming kernel K2. This particularconnection is labeled “K1→K2” in FIG. 5. A first table 501 (labeled“K1→K1”) shows the set of available/possible connections between kernelK1 and consuming kernel K2. Here, using the processor implementation ofFIG. 3 as the target architecture, all connections are possible. Thatis, assuming the first producing kernel K1 is mapped onto a particularone of processing cores 302_1 through 302_8, its corresponding consumingkernel K2 can be placed onto any of the other seven remaining processingcores.

First and second connections listed in table 501, labeled “Path_1” and“Path_2” respectively, correspond to kernel K1 sending its output datato either one of its neighboring cores (where consuming kernel K2operates). For example, referring to FIG. 3, if kernel K1 is operatingon core 302_2, Path_1 corresponds to kernel K2 operating on core 302_1and Path_2 corresponds to kernel K2 operating on core 302_3. Two morepaths, Path_3 and Path_4 correspond to kernel K1's output data beingsent to one of the processing cores that reside on the opposite side ofthe cores that neighbor K1's core. For example, again assuming kernel K1is operating on core 301_2, Path_3 corresponds to kernel K2 operating oncore 302_4 and Path_4 corresponds to kernel K2 operating on core 302_8.

Here, note that both Path_1 and Path_2 are each assigned a nodal hopdistance of 1.0. One unit of nodal hop distance corresponds to onelogical unit length along the network ring 304. That is, the distancefrom a core to either of its immediate neighbors is assigned a nodal hopdistance of 1.0. Because both Path_1 and Path_2 send K1's output data toone of its neighboring cores, both of these paths are assigned a nodalhop distance of 1.0. By contrast, each of Path_3 and Path_4 have a nodalhop distance of 2.0 in table 501 because, as discussed above, Path_3 andPath_4 correspond to kernel K1's data being forwarded two core locationsaround the ring from the core that kernel K1 operates from.

Continuing with this approach, Path_5 and Path_6 correspond to theforwarding of K1's output data to either of the cores that are threenodal hops away from K1's core (these paths have a nodal hop distance of3.0). Finally, Path_7 has a nodal hop distance of 4.0 and corresponds tothe (single) path that is oppositely positioned from K1's core on thenetwork ring. For example, again using the example where kernel K1 isoperating on core 302_2, Path_8 corresponds to the output data of kernelK1 being forwarded to core 302_6.

Each of the paths listed in table 501 have an associated weight whichcorresponds to the multiple of the nodal hop distance and the size ofthe image data that is being transferred along the connection. In thecase of the K1 to K2 connection, from the depiction of the applicationsoftware program in FIG. 4, the image data has a size of 4.0. Therefore,each nodal hop distance of a particular path that is listed in FIG. 4 ismultiplied by 4.0 to determine the total weight for the path. The K1→K2table 501 therefore lists the total weights for all possible paths fromkernel K1 to the various other cores within the processor that K1 cansend its data to.

Continuing then, with the K1→K2 table 501 describing the differentpossible paths for the K1→K2 connection, FIG. 5 further demonstrates thenext level of analysis performed by the affinity mapper for one of thesepaths. Here, the K2→K3_1 table 502 shows the remaining availableconnections that may be used for the K2→K3_1 connection (the sending ofK2's output data to the K3_1 kernel) for Path_1 from the K1→K2 table501. Recalling from above that Path_1 corresponds to the configurationwhere K1 forwards its output data to a core that neighbors the core thatis executing kernel K1, the K2→K3_1 table 502 shows the remaining pathoptions if this specific configuration is at play.

Here, note that the K2→K3_1 table 502 has only one path, Path_11, havinga nodal hop of 1. This is a consequence of one of the cores thatcorresponds to a nodal hop of 1 being already consumed executing kernelK1 and is therefore not available to receive data from kernel K2 (theinstant example is assuming different kernels will execute on differentcores). Said another way, the purpose of the specific K2→K3_1 table 502of FIG. 5 is to effectively place kernel K3_1 on an available corerecognizing that the core that kernel K1 is executing on is notavailable.

Here, Path_11 correspond to kernel K3_1 being placed on the oneremaining available core that immediately neighbors the core that kernelK2 is executing on. Again, recalling the example for Path_1 where kernelK1 executes on core 302_2 of FIG. 3 and kernel K1 forwards its outputdata to kernel K2 which operations on core 301_2, Path_11 corresponds tokernel K2 sending its output data to core 301_3 where K3_1 operates.Likewise, Path_12 and Path_13 correspond to K3_1 operating on one of thecores that is two hops away from the core that core K2 operates. Again,recognizing that Path_1 of FIG. 5 corresponds, e.g., to kernel K1operating on core 302_1 and kernel K2 operating on core 302_2, Path_12may correspond to kernel K3_1 operating on core 302_4 and Path_13 maycorrespond to kernel K3_1 operating on core 302_8. The remaining pathsPath_14 through Path_16 show the corresponding nodal hops as the corethat kernel K3_1 operates on moves farther away from the core thatkernel K2 operates on around the network ring. Note from FIG. 4 that theK2 to K3_1 connection maintains an image size of 1.0, and, as such, eachof the paths of table 501 have a total weight that is equal to the nodalhop distance (the nodal hop distance is factored by unity to determinethe total weight of the path).

Here, each unique path name corresponds to a unique path through theprocessor. Because all the paths listed in the K2→K3 table 502 emanatefrom Path_1 of the K1→K2 table 501, each of the paths listed in theK2→K3_1 table 502 necessarily include kernel K1 forwarding its outputdata to one of its neighboring core where K2 operates. Path_11 maytherefore be defined to include not only this connection but also aconnection to the only remaining available core that immediatelyneighbors the core that K2 operates on. Likewise, Path_12 may be definedto include the K1 to K2 single hop connection and a connection to one ofthe cores that is two hops away from K2's core (and Path_12 defined tobe to the other of such cores). Note that which specific core K1operates on need not be explicitly defined as the configuration can bedefined as offsets from the position of K1's core on the ring (whichevercore it ends up to be).

The K2→K3_1 table 502 observed in FIG. 5 only shows availableconnections when Path_1 of table 501 is at play. In various embodiments,the affinity mapper performs a similar next analysis for each of thepaths listed in the K1→K2 table 501. That is, noting that the K1→K2table 501 lists seven different paths, the affinity mapper wouldeffectively calculate seven tables like the K2→K3_1 table 502. Thesetables, however, would contain comparatively varied nodal hop and weightvalues to reflect their different corresponding base paths reflected intable 501. For example, the K2→K3_1 table 502 contains only one 1.0nodal hop listing because a core that neighbors the core that K2 isoperating on is not available to consume K2's data (because K1 isoperating on it). However, two 1.0 nodal hop listings would be availablefor any next table generated from any of the paths of the K1→K2 table501 other than Path_1.

FIG. 6 shows an example of deeper, next level calculations performed bythe affinity mapper that apply when Path_11 of FIG. 5 is in play. Here,recall that Path_11 corresponds to a configuration where K1 forwards itsoutput data to one of its immediately neighboring cores (where K2operates) and K2 forwards its output data to the only remainingavailable neighboring core (where K3_1 operates). For example, if K1operates on core 302_2 of FIG. 3 and K2 operates on core 302_3 thenPath_11 necessarily includes K3_1 operating on core 302_4. The nextlevel of calculation, depicted as the K2→K3_2 table 601 of FIG. 6,determines to which core K2 will forward its data for consumption byK3_2 if Path_11 is in play. Referring to table 601, note that no pathshaving a nodal hop of 1.0 are available. Here, under the configurationof Path_11, both of the cores that neighbor K2's core are being utilized(one to execute K1 and the other to execute K3_1). As such, the closestcores are 2.0 nodal hops away. The total weights of the table 601 arealso factored by unity because, from FIG. 4, the size of the imageframes that are sent from kernel K2 to kernel K3_2 are also 1.0.

The K3_1→K4 table 602 lists the possible paths and associated totalweights if Path_112 of the K2→K3_2 path of table 601 is in play. Here,Path_112 corresponds to a configuration in which K1 and K3_1 operate oncores that immediately neighbor K2's core and K3_2 operates on a corethat immediately neighbors K1's core or that immediately neighborsK3_1's core. Assuming Path_112 corresponds to a configuration in whichK3_2 operates on the core that is next to K1's core, K4 will have fourremaining cores upon which to operate on, one core that is one hop awayfrom K3_1's core, one core that is two hops away from K3_1's core, onecore that is three hops away from K3_1's core and one core that is fourhops away from K3_1's core. For example, if K1 operates on core 302_2,K2 operates on core 302_3, K3_1 operates on core 302_4 and K3_2 operateson core 302_1, K4 could be placed on any of cores 302_5, 302_6, 302_7,302_8 which are 1.0, 2.0, 3.0 and 4.0 nodal hops away respectively fromK3_1's core (302_4). Table 604 reflects these options with appropriateweights. Placing K4 completes the kernel to core mapping for theapplication structure of FIG. 4.

Note that passing down through each level of calculations from FIG. 5through FIG. 6 the number of available paths continually decreasesreflecting the existing commitment of cores to the particularconfiguration represented by the thread of continually deepercalculations. Again, in various embodiments, the affinity mapperexplores/calculates all levels from all possible connections. Eachthread of unique calculations through the various levels corresponds toa different configuration of particular kernels on particular cores.Each thread accumulates the total weight along its particular set ofselected paths which results in a final weight for the completethread/configuration. The thread with the lowest total weight isselected as the configuration of the application software program forthe processor.

In various embodiments, after the kernel to core mappings have beendefined, buffers (queues) are allocated onto the line buffer units.Recall from the discussion of FIGS. 2 and 3 that a line buffer unitreceives, e.g., line groups of image data sent from a producing kerneland queues the line groups before forwarding them to the consumingkernel. Here, the queuing for a single producer/consumer connection maybe referred to as a “buffer”. A buffer has an associated amount of linebuffer unit memory space that it consumes in order to implement itscorresponding queue. Here, a single line buffer unit can be configuredto implement multiple buffers. In an embodiment, each of the line bufferunits have a limited amount of memory space such that the total size ofall buffers that are allocated to a particular line buffer unit shouldfit within the line buffer unit's memory resources.

In an embodiment, each buffer in the application software program andits corresponding memory consumption footprint is defined. Then, foreach buffer, the mapping algorithm builds a list of line buffer unitssorted by the distance to the buffer's producing core (here, the linebuffer unit that is closest to the buffer's producing kernel's core islisted first and the line buffer unit that is farthest from the buffer'sproducing kernel's core is listed last). The algorithm then allocatesthe buffer to the highest ranked line buffer unit on the list that hasmemory space to accommodate the buffer. The mapping algorithm processeseach buffer in series according to this process until all buffers havebeen considered and allocated to a line buffer unit.

The kernel mapping and buffer allocation may be performed, e.g., by acompiler that compiles higher level application software program codeinto lower level object (executable) program code. The compilercalculates the total weights of, e.g., all possible threads representingall possible internal kernel configurations and connections so that thelowest thread/configuration can be identified, and, defines whichbuffers are allocated on which line buffer units. By so doing, thecompiler will have identified to which particular line buffer unit(s)each producing kernel on a particular core is to send its output dataand to which consuming kernel(s) on which core(s) each line buffer unitis to forward its queued data to. The identification includes anarticulation of which kernels are to be executed on which cores (or atleast positional offsets of the kernels from one another). The chosenconfiguration is recorded, e.g., in meta-data that accompanies thecompiled application software. The meta-data is then used to, e.g.,enter specific values into the kernels and/or configuration registerspace of the processor to physically effect the selected configuration,as part of the loading of the loading of the application softwareprogram on the image processor for execution.

Although the above described approach of FIGS. 5 and 6 have beendirected to assigning a weight for a kernel-to-kernel connection thatignores the presence of a line buffer unit that stores and forwards thedata over the course of the connection, other embodiments may be furthergranularized such that a producing kernel to line buffer unit weight isdetermined and a line buffer unit to consuming kernel weight isdetermined for a kernel-to-kernel connection. The search space issignificantly expanded with such an approach however as compared to theapproach of FIGS. 5 and 6. Buffer allocations may also be assigned tosuch connections which may add to the search space overhead.

Although the discussion of FIGS. 5 and 6 have been directed todetermining an application software program configuration for a hardwareplatform having the image processor architecture of FIG. 3, note thatthe teachings above can be applied to various other alternativeembodiments. For example, the image processor implementation of FIG. 3has equal numbers of line buffer units and cores. Other implementationsmay have different numbers of line buffer units and cores.

Further still, as discussed above, the line buffer units 301 forwardgroups of lines of an image. Alternative implementations need notnecessarily receive and forward line groups specifically). Also,although the image processor of FIG. 3 includes a ring network 304,other types of networks may be used (e.g., a switched network, atraditional multi-drop bus, etc.). Even further, the cores need notinclude a respective sheet generator or stencil processor havingtwo-dimensional execution lane array or two-dimensional shift registerarray.

FIG. 7 shows a method described above. The method includes calculatingdata transfer metrics for kernel-to-kernel connections of a programhaving a plurality of kernels that is to execute on an image processor701. The image processor includes a plurality of processing cores and anetwork connecting the plurality of processing cores. Each of thekernel-to-kernel connections include a producing kernel that is toexecute on one of the processing cores and a consuming kernel that is toexecute on another one of the processing cores. The consuming kernel isto operate on data generated by the producing kernel. The method alsoincludes assigning kernels of the plurality of kernels to respectiveones of the processing cores based on the calculated data transfermetrics 702.

3.0 Image Processor Implementation Embodiments

FIGS. 8a-e through FIG. 12 provide additional details concerningoperation and design of various embodiments for the image processor andassociated stencil processor described at length above. Recalling fromthe discussion of FIG. 2 that a line buffer unit feeds line groups to astencil processor's associated sheet generator, FIGS. 8a through 8eillustrate at a high level embodiments of both the parsing activity of aline buffer unit 201, the finer grained parsing activity of a sheetgenerator unit 203 as well as the stencil processing activity of thestencil processor that is coupled to the sheet generator unit 203.

FIG. 8a depicts an embodiment of an input frame of image data 801. FIG.8a also depicts an outline of three overlapping stencils 802 (eachhaving a dimension of 3 pixels×3 pixels) that a stencil processor isdesigned to operate over. The output pixel that each stencilrespectively generates output image data for is highlighted in solidblack. For simplicity, the three overlapping stencils 802 are depictedas overlapping only in the vertical direction. It is pertinent torecognize that in actuality a stencil processor may be designed to haveoverlapping stencils in both the vertical and horizontal directions.

Because of the vertical overlapping stencils 802 within the stencilprocessor, as observed in FIG. 8a , there exists a wide band of imagedata within the frame that a single stencil processor can operate over.As will be discussed in more detail below, in an embodiment, the stencilprocessors process data within their overlapping stencils in a left toright fashion across the image data (and then repeat for the next set oflines, in top to bottom order). Thus, as the stencil processors continueforward with their operation, the number of solid black output pixelblocks will grow right-wise horizontally. As discussed above, a linebuffer unit 201 is responsible for parsing a line group of input imagedata from an incoming frame that is sufficient for the stencilprocessors to operate over for an extended number of upcoming cycles. Anexemplary depiction of a line group is illustrated as a shaded region803. In an embodiment, the line buffer unit 201 can comprehend differentdynamics for sending/receiving a line group to/from a sheet generator.For example, according to one mode, referred to as “full group”, thecomplete full width lines of image data are passed between a line bufferunit and a sheet generator. According to a second mode, referred to as“virtually tall”, a line group is passed initially with a subset of fullwidth rows. The remaining rows are then passed sequentially in smaller(less than full width) pieces.

With the line group 803 of the input image data having been defined bythe line buffer unit and passed to the sheet generator unit, the sheetgenerator unit further parses the line group into finer sheets that aremore precisely fitted to the hardware limitations of the stencilprocessor. More specifically, as will be described in more detailfurther below, in an embodiment, each stencil processor consists of atwo dimensional shift register array. The two dimensional shift registerarray essentially shifts image data “beneath” an array of executionlanes where the pattern of the shifting causes each execution lane tooperate on data within its own respective stencil (that is, eachexecution lane processes on its own stencil of information to generatean output for that stencil). In an embodiment, sheets are surface areasof input image data that “fill” or are otherwise loaded into the twodimensional shift register array.

As will be described in more detail below, in various embodiments, thereare actually multiple layers of two dimensional register data that canbe shifted on any cycle. For convenience, much of the presentdescription will simply use the term “two-dimensional shift register”and the like to refer to structures that have one or more such layers oftwo-dimensional register data that can be shifted.

Thus, as observed in FIG. 8b , the sheet generator parses an initialsheet 804 from the line group 803 and provides it to the stencilprocessor (here, the sheet of data corresponds to the shaded region thatis generally identified by reference number 804). As observed in FIGS.8c and 8d , the stencil processor operates on the sheet of input imagedata by effectively moving the overlapping stencils 802 in a left toright fashion over the sheet. As of FIG. 8d , the number of pixels forwhich an output value could be calculated from the data within the sheetis exhausted (no other pixel positions can have an output valuedetermined from the information within the sheet). For simplicity theborder regions of the image have been ignored.

As observed in FIG. 8e the sheet generator then provides a next sheet805 for the stencil processor to continue operations on. Note that theinitial positions of the stencils as they begin operation on the nextsheet is the next progression to the right from the point of exhaustionon the first sheet (as depicted previously in FIG. 8d ). With the newsheet 805, the stencils will simply continue moving to the right as thestencil processor operates on the new sheet in the same manner as withthe processing of the first sheet.

Note that there is some overlap between the data of the first sheet 804and the data of the second sheet 805 owing to the border regions ofstencils that surround an output pixel location. The overlap could behandled simply by the sheet generator re-transmitting the overlappingdata twice. In alternate implementations, to feed a next sheet to thestencil processor, the sheet generator may proceed to only send new datato the stencil processor and the stencil processor reuses theoverlapping data from the previous sheet.

FIG. 9 shows an embodiment of a stencil processor architecture 900. Asobserved in FIG. 9, the stencil processor includes a data computationunit 901, a scalar processor 902 and associated memory 903 and an I/Ounit 904. The data computation unit 901 includes an array of executionlanes 905, a two-dimensional shift array structure 906 and separaterandom access memories 907 associated with specific rows or columns ofthe array.

The I/O unit 904 is responsible for loading “input” sheets of datareceived from the sheet generator into the data computation unit 901 andstoring “output” sheets of data from the stencil processor into thesheet generator. In an embodiment the loading of sheet data into thedata computation unit 901 entails parsing a received sheet intorows/columns of image data and loading the rows/columns of image datainto the two dimensional shift register structure 906 or respectiverandom access memories 907 of the rows/columns of the execution lanearray (described in more detail below). If the sheet is initially loadedinto memories 907, the individual execution lanes within the executionlane array 905 may then load sheet data into the two-dimensional shiftregister structure 906 from the random access memories 907 whenappropriate (e.g., as a load instruction just prior to operation on thesheet's data). Upon completion of the loading of a sheet of data intothe register structure 906 (whether directly from a sheet generator orfrom memories 907), the execution lanes of the execution lane array 905operate on the data and eventually “write back” finished data as a sheetdirectly back to the sheet generator, or, into the random accessmemories 907. If the later the I/O unit 904 fetches the data from therandom access memories 907 to form an output sheet which is thenforwarded to the sheet generator.

The scalar processor 902 includes a program controller 909 that readsthe instructions of the stencil processor's program code from scalarmemory 903 and issues the instructions to the execution lanes in theexecution lane array 905. In an embodiment, a single same instruction isbroadcast to all execution lanes within the array 905 to effect aSIMD-like behavior from the data computation unit 901. In an embodiment,the instruction format of the instructions read from scalar memory 903and issued to the execution lanes of the execution lane array 905includes a very-long-instruction-word (VLIW) type format that includesmore than one opcode per instruction. In a further embodiment, the VLIWformat includes both an ALU opcode that directs a mathematical functionperformed by each execution lane's ALU (which, as described below, in anembodiment may specify more than one traditional ALU operation) and amemory opcode (that directs a memory operation for a specific executionlane or set of execution lanes).

The term “execution lane” refers to a set of one or more execution unitscapable of executing an instruction (e.g., logic circuitry that canexecute an instruction). An execution lane can, in various embodiments,include more processor-like functionality beyond just execution units,however. For example, besides one or more execution units, an executionlane may also include logic circuitry that decodes a receivedinstruction, or, in the case of more MIMD-like designs, logic circuitrythat fetches and decodes an instruction. With respect to MIMD-likeapproaches, although a centralized program control approach has largelybeen described herein, a more distributed approach may be implemented invarious alternative embodiments (e.g., including program code and aprogram controller within each execution lane of the array 905).

The combination of an execution lane array 905, program controller 909and two dimensional shift register structure 906 provides a widelyadaptable/configurable hardware platform for a broad range ofprogrammable functions. For example, application software developers areable to program kernels having a wide range of different functionalcapability as well as dimension (e.g., stencil size) given that theindividual execution lanes are able to perform a wide variety offunctions and are able to readily access input image data proximate toany output array location.

Apart from acting as a data store for image data being operated on bythe execution lane array 905, the random access memories 907 may alsokeep one or more look-up tables. In various embodiments one or morescalar look-up tables may also be instantiated within the scalar memory903.

A scalar look-up involves passing the same data value from the samelook-up table from the same index to each of the execution lanes withinthe execution lane array 905. In various embodiments, the VLIWinstruction format described above is expanded to also include a scalaropcode that directs a look-up operation performed by the scalarprocessor into a scalar look-up table. The index that is specified foruse with the opcode may be an immediate operand or fetched from someother data storage location. Regardless, in an embodiment, a look-upfrom a scalar look-up table within scalar memory essentially involvesbroadcasting the same data value to all execution lanes within theexecution lane array 905 during the same clock cycle. Additional detailsconcerning use and operation of look-up tables is provided furtherbelow.

FIG. 9b summarizes the VLIW instruction word embodiments(s) discussedabove. As observed in FIG. 9b , the VLIW instruction word formatincludes fields for three separate instructions: 1) a scalar instruction951 that is executed by the scalar processor; 2) an ALU instruction 952that is broadcasted and executed in SIMD fashion by the respective ALUswithin the execution lane array; and, 3) a memory instruction 953 thatis broadcasted and executed in a partial SIMD fashion (e.g., ifexecution lanes along a same row in the execution lane array share asame random access memory, then one execution lane from each of thedifferent rows actually execute the instruction (the format of thememory instruction 953 may include an operand that identifies whichexecution lane from each row executes the instruction).

A field 954 for one or more immediate operands is also included. Whichof the instructions 951, 952, 953 use which immediate operandinformation may be identified in the instruction format. Each ofinstructions 951, 952, 953 also include their own respective inputoperand and resultant information (e.g., local registers for ALUoperations and a local register and a memory address for memory accessinstructions). In an embodiment, the scalar instruction 951 is executedby the scalar processor before the execution lanes within the executionlane array execute either of the other to instructions 952, 953. Thatis, the execution of the VLIW word includes a first cycle upon which thescalar instruction 951 is executed followed by a second cycle upon withthe other instructions 952, 953 may be executed (note that in variousembodiments instructions 952 and 953 may be executed in parallel).

In an embodiment, the scalar instructions executed by the scalarprocessor include commands issued to the sheet generator to load/storesheets from/into the memories or 2D shift register of the datacomputation unit. Here, the sheet generator's operation can be dependenton the operation of the line buffer unit or other variables that preventpre-runtime comprehension of the number of cycles it will take the sheetgenerator to complete any command issued by the scalar processor. Assuch, in an embodiment, any VLIW word whose scalar instruction 951corresponds to or otherwise causes a command to be issued to the sheetgenerator also includes no-operation (NOOP) instructions in the othertwo instruction field 952, 953. The program code then enters a loop ofNOOP instructions for instruction fields 952, 953 until the sheetgenerator completes its load/store to/from the data computation unit.Here, upon issuing a command to the sheet generator, the scalarprocessor may set a bit of an interlock register that the sheetgenerator resets upon completion of the command. During the NOOP loopthe scalar processor monitors the bit of the interlock bit. When thescalar processor detects that the sheet generator has completed itscommand normal execution begins again.

FIG. 10 shows an embodiment of a data computation component 1001. Asobserved in FIG. 10, the data computation component 1001 includes anarray of execution lanes 1005 that are logically positioned “above” atwo-dimensional shift register array structure 1006. As discussed above,in various embodiments, a sheet of image data provided by a sheetgenerator is loaded into the two-dimensional shift register 1006. Theexecution lanes then operate on the sheet data from the registerstructure 1006.

The execution lane array 1005 and shift register structure 1006 arefixed in position relative to one another. However, the data within theshift register array 1006 shifts in a strategic and coordinated fashionto cause each execution lane in the execution lane array to process adifferent stencil within the data. As such, each execution lanedetermines the output image value for a different pixel in the outputsheet being generated. From the architecture of FIG. 10 it should beclear that overlapping stencils are not only arranged vertically butalso horizontally as the execution lane array 1005 includes verticallyadjacent execution lanes as well as horizontally adjacent executionlanes.

Some notable architectural features of the data computation unit 1001include the shift register structure 1006 having wider dimensions thanthe execution lane array 1005. That is, there is a “halo” of registers1009 outside the execution lane array 1005. Although the halo 1009 isshown to exist on two sides of the execution lane array, depending onimplementation, the halo may exist on less (one) or more (three or four)sides of the execution lane array 1005. The halo 1005 serves to provide“spill-over” space for data that spills outside the bounds of theexecution lane array 1005 as the data is shifting “beneath” theexecution lanes 1005. As a simple case, a 5×5 stencil centered on theright edge of the execution lane array 1005 will need four halo registerlocations further to the right when the stencil's leftmost pixels areprocessed. For ease of drawing, FIG. 10 shows the registers of the rightside of the halo as only having horizontal shift connections andregisters of the bottom side of the halo as only having vertical shiftconnections when, in a nominal embodiment, registers on either side(right, bottom) would have both horizontal and vertical connections. Invarious embodiments, the halo region does not include correspondingexecution lane logic to execute image processing instructions (e.g., noALU is present). However, individual memory access units (M) are presentin each of the halo region locations so that the individual haloregister locations can individually load data from memory and store datato memory.

Additional spill-over room is provided by random access memories 1007that are coupled to each row and/or each column in the array, orportions thereof (E.g., a random access memory may be assigned to a“region” of the execution lane array that spans 4 execution lanes rowwise and 2 execution lanes column wise. For simplicity the remainder ofthe application will refer mainly to row and/or column based allocationschemes). Here, if a execution lane's kernel operations require it toprocess pixel values outside of the two-dimensional shift register array1006 (which some image processing routines may require) the plane ofimage data is able to further spill-over, e.g., from the halo region1009 into random access memory 1007. For example, consider a 6×6 stencilwhere the hardware includes a halo region of only four storage elementsto the right of a execution lane on the right edge of the execution lanearray. In this case, the data would need to be shifted further to theright off the right edge of the halo 1009 to fully process the stencil.Data that is shifted outside the halo region 1009 would then spill-overto random access memory 1007. Other applications of the random accessmemories 1007 and the stencil processor of FIG. 9 are provided furtherbelow.

FIGS. 11a through 11k demonstrate a working example of the manner inwhich image data is shifted within the two dimensional shift registerarray “beneath” the execution lane array as alluded to above. Asobserved in FIG. 11a , the data contents of the two dimensional shiftarray are depicted in a first array 1107 and the execution lane array isdepicted by a frame 1105. Also, two neighboring execution lanes 1110within the execution lane array are simplistically depicted. In thissimplistic depiction 1110, each execution lane includes a register R1that can accept data from the shift register, accept data from an ALUoutput (e.g., to behave as an accumulator across cycles), or writeoutput data into an output destination.

Each execution lane also has available, in a local register R2, thecontents “beneath” it in the two dimensional shift array. Thus, R1 is aphysical register of the execution lane while R2 is a physical registerof the two dimensional shift register array. The execution lane includesan ALU that can operate on operands provided by R1 and/or R2. As will bedescribed in more detail further below, in an embodiment the shiftregister is actually implemented with multiple (a “depth” of)storage/register elements per array location but the shifting activityis limited to one plane of storage elements (e.g., only one plane ofstorage elements can shift per cycle). FIGS. 11a through 11k depict oneof these deeper register locations as being used to store the resultantX from the respective execution lanes. For illustrative ease the deeperresultant register is drawn alongside rather than beneath itscounterpart register R2.

FIGS. 11a through 11k focus on the calculation of two stencils whosecentral position is aligned with the pair of execution lane positions1111 depicted within the execution lane array. For ease of illustration,the pair of execution lanes 1110 are drawn as horizontal neighbors whenin fact, according to the following example, they are verticalneighbors.

As observed initially in FIG. 11a , the execution lanes are centered ontheir central stencil locations. FIG. 11b shows the object code executedby both execution lanes. As observed in FIG. 11b the program code ofboth execution lanes causes the data within the shift register array toshift down one position and shift right one position. This aligns bothexecution lanes to the upper left hand corner of their respectivestencils. The program code then causes the data that is located (in R2)in their respective locations to be loaded into R1.

As observed in FIG. 11c the program code next causes the pair ofexecution lanes to shift the data within the shift register array oneunit to the left which causes the value to the right of each executionlane's respective position to be shifted into each execution lane'position. The value in R1 (previous value) is then added with the newvalue that has shifted into the execution lane's position (in R2). Theresultant is written into R1. As observed in FIG. 11d the same processas described above for FIG. 11c is repeated which causes the resultantR1 to now include the value A+B+C in the upper execution lane and F+G+Hin the lower execution lane. At this point both execution lanes haveprocessed the upper row of their respective stencils. Note thespill-over into a halo region on the left side of the execution lanearray (if one exists on the left hand side) or into random access memoryif a halo region does not exist on the left hand side of the executionlane array.

As observed in FIG. 11e , the program code next causes the data withinthe shift register array to shift one unit up which causes bothexecution lanes to be aligned with the right edge of the middle row oftheir respective stencils. Register R1 of both execution lanes currentlyincludes the summation of the stencil's top row and the middle row'srightmost value. FIGS. 11f and 11g demonstrate continued progress movingleftwise across the middle row of both execution lane's stencils. Theaccumulative addition continues such that at the end of processing ofFIG. 11g both execution lanes include the summation of the values of thetop row and the middle row of their respective stencils.

FIG. 11h shows another shift to align each execution lane with itscorresponding stencil's lowest row. FIGS. 11i and 11j show continuedshifting to complete processing over the course of both execution lanes'stencils. FIG. 11k shows additional shifting to align each executionlane with its correct position in the data array and write the resultantthereto.

In the example of FIGS. 11a-11k note that the object code for the shiftoperations may include an instruction format that identifies thedirection and magnitude of the shift expressed in (X, Y) coordinates.For example, the object code for a shift up by one location may beexpressed in object code as SHIFT 0, +1. As another example, a shift tothe right by one location may expressed in object code as SHIFT+1, 0. Invarious embodiments shifts of larger magnitude may also be specified inobject code (e.g., SHIFT 0, +2). Here, if the 2D shift register hardwareonly supports shifts by one location per cycle, the instruction may beinterpreted by the machine to require multiple cycle execution, or, the2D shift register hardware may be designed to support shifts by morethan one location per cycle. Embodiments of the later are described inmore detail further below.

FIG. 12 shows another, more detailed depiction of the unit cell for anexecution lane and corresponding shift register structure (registers inthe halo region do not include a corresponding execution lane but doinclude a memory unit in various embodiments). The execution lane andthe register space associated with each location in the execution lanearray is, in an embodiment, implemented by instantiating the circuitryobserved in FIG. 12 at each node of the execution lane array. Asobserved in FIG. 12, the unit cell includes a execution lane 1201coupled to a register file 1202 consisting of four registers R2 throughR5. During any cycle, the execution lane 1201 may read from or write toany of registers R1 through R5. For instructions requiring two inputoperands the execution lane may retrieve both of operands from any of R1through R5.

In an embodiment, the two dimensional shift register structure isimplemented by permitting, during a single cycle, the contents of any of(only) one of registers R2 through R4 to be shifted “out” to one of itsneighbor's register files through output multiplexer 1203, and, havingthe contents of any of (only) one of registers R2 through R4 replacedwith content that is shifted “in” from a corresponding one if itsneighbors through input multiplexers 1204 such that shifts betweenneighbors are in a same direction (e.g., all execution lanes shift left,all execution lanes shift right, etc.). Although it may be common for asame register to have its contents shifted out and replaced with contentthat is shifted in on a same cycle, the multiplexer arrangement 1203,1204 permits for different shift source and shift target registerswithin a same register file during a same cycle.

As depicted in FIG. 12 note that during a shift sequence a executionlane will shift content out from its register file 1202 to each of itsleft, right, top and bottom neighbors. In conjunction with the sameshift sequence, the execution lane will also shift content into itsregister file from a particular one of its left, right, top and bottomneighbors. Again, the shift out target and shift in source should beconsistent with a same shift direction for all execution lanes (e.g., ifthe shift out is to the right neighbor, the shift in should be from theleft neighbor).

Although in one embodiment the content of only one register is permittedto be shifted per execution lane per cycle, other embodiments may permitthe content of more than one register to be shifted in/out. For example,the content of two registers may be shifted out/in during a same cycleif a second instance of the multiplexer circuitry 1203, 1204 observed inFIG. 12 is incorporated into the design of FIG. 12. Of course, inembodiments where the content of only one register is permitted to beshifted per cycle, shifts from multiple registers may take place betweenmathematical operations by consuming more clock cycles for shiftsbetween mathematical operations (e.g., the contents of two registers maybe shifted between math ops by consuming two shift ops between the mathops).

If less than all the content of a execution lane's register files areshifted out during a shift sequence note that the content of the nonshifted out registers of each execution lane remain in place (do notshift). As such, any non shifted content that is not replaced withshifted in content persists local to the execution lane across theshifting cycle. The memory unit (“M”) observed in each execution lane isused to load/store data from/to the random access memory space that isassociated with the execution lane's row and/or column within theexecution lane array. Here, the M unit acts as a standard M unit in thatit is often used to load/store data that cannot be loaded/stored from/tothe execution lane's own register space. In various embodiments, theprimary operation of the M unit is to write data from a local registerinto memory, and, read data from memory and write it into a localregister.

With respect to the ISA opcodes supported by the ALU unit of thehardware execution lane 1201, in various embodiments, the mathematicalopcodes supported by the hardware ALU include (e.g., ADD, SUB, MOV, MUL,MAD, ABS, DIV, SHL, SHR, MIN/MAX, SEL, AND, OR, XOR, NOT). As describedjust above, memory access instructions can be executed by the executionlane 1201 to fetch/store data from/to their associated random accessmemory. Additionally the hardware execution lane 1201 supports shift opinstructions (right, left, up, down) to shift data within the twodimensional shift register structure. As described above, programcontrol instructions are largely executed by the scalar processor of thestencil processor.

4.0 Implementation Embodiments

It is pertinent to point out that the various image processorarchitecture features described above are not necessarily limited toimage processing in the traditional sense and therefore may be appliedto other applications that may (or may not) cause the image processor tobe re-characterized. For example, if any of the various image processorarchitecture features described above were to be used in the creationand/or generation and/or rendering of animation as opposed to theprocessing of actual camera images, the image processor may becharacterized as a graphics processing unit. Additionally, the imageprocessor architectural features described above may be applied to othertechnical applications such as video processing, vision processing,image recognition and/or machine learning. Applied in this manner, theimage processor may be integrated with (e.g., as a co-processor to) amore general purpose processor (e.g., that is or is part of a CPU ofcomputing system), or, may be a stand alone processor within a computingsystem.

The hardware design embodiments discussed above may be embodied within asemiconductor chip and/or as a description of a circuit design foreventual targeting toward a semiconductor manufacturing process. In thecase of the later, such circuit descriptions may take of the form of a(e.g., VHDL or Verilog) register transfer level (RTL) circuitdescription, a gate level circuit description, a transistor levelcircuit description or mask description or various combinations thereof.Circuit descriptions are typically embodied on a computer readablestorage medium (such as a CD-ROM or other type of storage technology).

From the preceding sections is pertinent to recognize that an imageprocessor as described above may be embodied in hardware on a computersystem (e.g., as part of a handheld device's System on Chip (SOC) thatprocesses data from the handheld device's camera). In cases where theimage processor is embodied as a hardware circuit, note that the imagedata that is processed by the image processor may be received directlyfrom a camera. Here, the image processor may be part of a discretecamera, or, part of a computing system having an integrated camera. Inthe case of the later the image data may be received directly from thecamera or from the computing system's system memory (e.g., the camerasends its image data to system memory rather than the image processor).Note also that many of the features described in the preceding sectionsmay be applicable to a graphics processor unit (which rendersanimation).

FIG. 13 provides an exemplary depiction of a computing system. Many ofthe components of the computing system described below are applicable toa computing system having an integrated camera and associated imageprocessor (e.g., a handheld device such as a smartphone or tabletcomputer). Those of ordinary skill will be able to easily delineatebetween the two. Additionally, the computing system of FIG. 13 alsoincludes many features of a high performance computing system, such as aworkstation or supercomputer.

As observed in FIG. 13, the basic computing system may include a centralprocessing unit 1301 (which may include, e.g., a plurality of generalpurpose processing cores 1315_1 through 1315_N and a main memorycontroller 1317 disposed on a multi-core processor or applicationsprocessor), system memory 1302, a display 1303 (e.g., touchscreen,flat-panel), a local wired point-to-point link (e.g., USB) interface1304, various network I/O functions 1305 (such as an Ethernet interfaceand/or cellular modem subsystem), a wireless local area network (e.g.,WiFi) interface 1306, a wireless point-to-point link (e.g., Bluetooth)interface 1307 and a Global Positioning System interface 1308, varioussensors 1309_1 through 1309_N, one or more cameras 1310, a battery 1311,a power management control unit 1312, a speaker and microphone 1313 andan audio coder/decoder 1314.

An applications processor or multi-core processor 1350 may include oneor more general purpose processing cores 1315 within its CPU 1201, oneor more graphical processing units 1316, a memory management function1317 (e.g., a memory controller), an I/O control function 1318 and animage processing unit 1319. The general purpose processing cores 1315typically execute the operating system and application software of thecomputing system. The graphics processing units 1316 typically executegraphics intensive functions to, e.g., generate graphics informationthat is presented on the display 1303. The memory control function 1317interfaces with the system memory 1302 to write/read data to/from systemmemory 1302. The power management control unit 1312 generally controlsthe power consumption of the system 1300.

The image processing unit 1319 may be implemented according to any ofthe image processing unit embodiments described at length above in thepreceding sections.

Alternatively or in combination, the IPU 1319 may be coupled to eitheror both of the GPU 1316 and CPU 1301 as a co-processor thereof.Additionally, in various embodiments, the GPU 1316 may be implementedwith any of the image processor features described at length above. Theimage processing unit 1319 may be configured with application softwareas described at length above. Additionally, a computing system such asthe computing system of FIG. 13 may execute program code that performsthe calculations described above that determine the configuration of anapplication software program onto an image processor.

Each of the touchscreen display 1303, the communication interfaces1304-1307, the GPS interface 1308, the sensors 1309, the camera 1310,and the speaker/microphone codec 1313, 1314 all can be viewed as variousforms of I/O (input and/or output) relative to the overall computingsystem including, where appropriate, an integrated peripheral device aswell (e.g., the one or more cameras 1310). Depending on implementation,various ones of these I/O components may be integrated on theapplications processor/multi-core processor 1350 or may be located offthe die or outside the package of the applications processor/multi-coreprocessor 1350.

In an embodiment one or more cameras 1310 includes a depth cameracapable of measuring depth between the camera and an object in its fieldof view. Application software, operating system software, device driversoftware and/or firmware executing on a general purpose CPU core (orother functional block having an instruction execution pipeline toexecute program code) of an applications processor or other processormay perform any of the functions described above.

Embodiments of the invention may include various processes as set forthabove. The processes may be embodied in machine-executable instructions.The instructions can be used to cause a general-purpose orspecial-purpose processor to perform certain processes. Alternatively,these processes may be performed by specific hardware components thatcontain hardwired and/or programmable logic for performing theprocesses, or by any combination of programmed computer components andcustom hardware components.

Elements of the present invention may also be provided as amachine-readable medium for storing the machine-executable instructions.The machine-readable medium may include, but is not limited to, floppydiskettes, optical disks, CD-ROMs, and magneto-optical disks, FLASHmemory, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards,propagation media or other type of media/machine-readable mediumsuitable for storing electronic instructions. For example, the presentinvention may be downloaded as a computer program which may betransferred from a remote computer (e.g., a server) to a requestingcomputer (e.g., a client) by way of data signals embodied in a carrierwave or other propagation medium via a communication link (e.g., a modemor network connection).

In the foregoing specification, the invention has been described withreference to specific exemplary embodiments thereof. It will, however,be evident that various modifications and changes may be made theretowithout departing from the broader spirit and scope of the invention asset forth in the appended claims. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense.

1. A method, comprising: calculating data transfer metrics forkernel-to-kernel connections of a program comprising a plurality ofkernels that is to execute on an image processor, the image processorcomprising a plurality of processing cores and a network connecting theplurality of processing cores, each of the kernel-to-kernel connectionscomprising a producing kernel that is to execute on one of theprocessing cores and a consuming kernel that is to execute on anotherone of the processing cores, the consuming kernel to operate on datagenerated by the producing kernel; and, assigning kernels of theplurality of kernels to respective ones of the processing cores based onthe calculated data transfer metrics.
 2. The method of claim 1 whereinthe image processor further comprises a plurality of buffer units, thebuffer units to store and forward data of the kernel-to-kernelconnections.
 3. The method of claim 2 wherein the buffer units furthercomprise line buffer units to store and forward groups of lines of animage of a kernel-to-kernel connection.
 4. The method of claim 1 whereinthe calculating of the data transfer metrics further comprises assigningweights to the kernel-to-kernel connections based on numbers of nodalhops within the network between producing and consuming kernels.
 5. Themethod of claim 1 wherein the calculating of the data transfer metricsfurther comprises assigning weights to the kernel-to-kernel connectionsbased on sizes of images transferred over the network between producingand consuming kernels.
 6. The method of claim 1 wherein the assigning ofthe kernels to respective ones of the processing cores furthercomprises: calculating weights for different configurations of theprogram, each configuration of the program comprising a different set ofkernel assignments to the processing cores, wherein, calculation of aweight for a particular configuration is based on a subset of the datatransfer metrics calculated for the particular configuration'sparticular kernel-to-kernel connections; and, selecting one of theconfigurations having optimal weight.
 7. A non-transitory machinereadable storage medium containing program code that when processed by acomputing system causes the computing system to perform a method,comprising: calculating data transfer metrics for kernel-to-kernelconnections of a program comprising a plurality of kernels that is toexecute on an image processor, the image processor comprising aplurality of processing cores and a network connecting the plurality ofprocessing cores, each of the kernel-to-kernel connections comprising aproducing kernel that is to execute on one of the processing cores and aconsuming kernel that is to execute on another one of the processingcores, the consuming kernel to operate on data generated by theproducing kernel; and, assigning kernels of the plurality of kernels torespective ones of the processing cores based on the calculated datatransfer metrics.
 8. The non-transitory machine readable storage mediumof claim 7 wherein the image processor comprises a plurality of bufferunits, the buffer units to store and forward data of thekernel-to-kernel connections.
 9. The non-transitory machine readablestorage medium of claim 8 wherein the buffer units further comprise linebuffer units to store and forward groups of lines of an image of akernel-to-kernel connection.
 10. The non-transitory machine readablestorage medium of claim 7 wherein the calculating of the data transfermetrics further comprises assigning weights to the kernel-to-kernelconnections based on numbers of nodal hops within the network betweenproducing and consuming kernels.
 11. The non-transitory machine readablestorage medium of claim 7 wherein the calculating of the data transfermetrics further comprises assigning weights to the kernel-to-kernelconnections based on sizes of images transferred over the networkbetween producing and consuming kernels.
 12. The non-transitory machinereadable storage medium of claim 7 wherein the assigning of the kernelsto respective ones of the processing cores further comprises:calculating weights for different configurations of the program, eachconfiguration of the program comprising a different set of kernelassignments to the processing cores, wherein, calculation of a weightfor a particular configuration is based on a subset of the data transfermetrics calculated for the particular configuration's particularkernel-to-kernel connections; and, selecting one of the configurationshaving optimal weight.
 13. The non-transitory machine readable storagemedium of claim 7 wherein the processing cores comprise an executionlane array and a two dimensional shift register array.
 14. A computingsystem, comprising: a plurality of general purpose processing cores; asystem memory; a memory controller between the system memory and theplurality of general purpose processing cores; a non-transitory machinereadable storage medium containing program code that when processed bythe computing system causes the computing system to perform a method,comprising: calculating data transfer metrics for kernel-to-kernelconnections of a program comprising a plurality of kernels that is toexecute on an image processor, the image processor comprising aplurality of processing cores and a network connecting the plurality ofprocessing cores, each of the kernel-to-kernel connections comprising aproducing kernel that is to execute on one of the processing cores and aconsuming kernel that is to execute on another one of the processingcores, the consuming kernel to operate on data generated by theproducing kernel; and, assigning kernels of the plurality of kernels torespective ones of the processing cores based on the calculated datatransfer metrics.
 15. The computing system of claim 14 wherein the imageprocessor comprises a plurality of buffer units, the buffer units tostore and forward data of the kernel-to-kernel connections.
 16. Thecomputing system of claim 15 wherein the buffer units further compriseline buffer units to store and forward groups of lines of an image of akernel-to-kernel connection.
 17. The computing system of claim 14wherein the calculating of the data transfer metrics further comprisesassigning weights to the kernel-to-kernel connections based on numbersof nodal hops within the network between producing and consumingkernels.
 18. The computing system of claim 14 wherein the calculating ofthe data transfer metrics further comprises assigning weights to thekernel-to-kernel connections based on sizes of images transferred overthe network between producing and consuming kernels.
 19. The computingsystem of claim 14 wherein the assigning of the kernels to respectiveones of the processing cores further comprises: calculating weights fordifferent configurations of the program, each configuration of theprogram comprising a different set of kernel assignments to theprocessing cores, wherein, calculation of a weight for a particularconfiguration is based on a subset of the data transfer metricscalculated for the particular configuration's particularkernel-to-kernel connections; and, selecting one of the configurationshaving optimal weight.
 20. The computing system of claim 14 wherein theprocessing cores comprise an execution lane array and a two dimensionalshift register array.